Improving natural language information extraction from cancer pathology reports using transfer learning and zero-shot string similarity.

ABSTRACT:

Objective

We develop natural language processing (NLP) methods capable of accurately classifying tumor attributes from pathology reports given minimal labeled examples. Our hierarchical cancer to cancer transfer (HCTC) and zero-shot string similarity (ZSS) methods are designed to exploit shared information between cancers and auxiliary class features, respectively, to boost performance using enriched annotations which give both location-based information and document level labels for each pathology report.

Materials and methods

Our data consists of 250 pathology reports each for kidney, colon, and lung cancer from 2002 to 2019 from a single institution (UCSF). For each report, we classified 5 attributes: procedure, tumor location, histology, grade, and presence of lymphovasc

SUBMITTER: Park B 

PROVIDER: S-EPMC8484934 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature